Insurance companies may maintain insurance data and account information for thousands or millions of customers. Each customer may be issued an insurance policy number and a premium amount based on the particular insurance policy. The premium amount for a particular insurance policy may be calculated based on a variety of factors, such as a territory factor (e.g., a zip code/location of an asset), age factor (e.g., number of months in the life of a person or asset), vehicle factor, claim history factor, and other factors known to a person having ordinary skill in the art.
In order to calculate premium amounts and other rates, insurance companies may employ algorithms to score insurance data. In a traditional insurance system, each of the aforementioned insurance factors may be assessed, and the system obtains/assigns values for each factor. The system then engages in various calculations to determine an insurance rate/premium to assign to the insurance policy. There may be numerous insurance factors being assessed for thousands or millions of insurance policy-holding customers, resulting in a large amount of data (e.g., big data). The requisite calculations and scoring may entail processing the large amount of data and may necessitate substantial coordination with one or more systems and processor-intensive, time-consuming calculations. As such, there is room for improvement of these prior art systems and methods of scoring insurance rates/premiums.